This paper proposes a real-time monitoring framework for a landslide susceptibility area based on wireless sensor network using multiple Unmanned Aerial Vehicles (UAVs). Many researchers have considered building a landslide susceptibility map to distinguish different levels of landslide susceptible zones. However, to prevent damage from landslides, it is more important for the disaster control center to identify the time and location of the landslide occurrence in those highly susceptible areas. Hence, a rain-triggered landslide monitoring system is proposed herein for local mountain areas. First, a wireless sensor network framework is constructed to inform the control center as immediately as possible when landslides occur. Second, multiple UAV sensors will be responsible for collecting the stereo images of the slope in highly sensitive zones on schedule. Based on the stereo images and the binocular model, in-depth information can be obtained. With the depth information and Speeded Up Robust Features (SURF) detection, the key point characteristic information is constructed as the input data for Support Vector Machine (SVM). An SVM algorithm is designed with Python program language and executed in real time. Using this algorithm, the real-time images collected by UAVs and the landslide warning information will be sent to the control center for further analysis. Finally, a field experiment is conducted to demonstrate the effectiveness of the proposed method.

中文翻译：

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用于降雨诱发滑坡检测的无人机线性传感器网络框架

本文提出了一种基于无线传感器网络的滑坡易发区实时监测框架，该框架使用多架无人机（UAV）。许多研究人员已经考虑建立滑坡敏感性图来区分不同级别的滑坡易感区。然而，为了防止滑坡造成的破坏，灾害控制中心更重要的是要确定那些高度易发地区滑坡发生的时间和地点。因此，本文针对局部山区提出了一种降雨触发的滑坡监测系统。首先，构建无线传感器网络框架，以在滑坡发生时尽快通知控制中心。第二，多个无人机传感器将负责按时采集高度敏感区域斜坡的立体图像。基于立体图像和双目模型，可以获得深度信息。通过深度信息和加速鲁棒特征（SURF）检测，构建关键点特征信息作为支持向量机（SVM）的输入数据。支持向量机算法是用 Python 程序语言设计并实时执行的。使用该算法，无人机采集的实时图像和滑坡预警信息将被发送到控制中心进行进一步分析。最后，通过现场实验验证了所提方法的有效性。通过深度信息和加速鲁棒特征（SURF）检测，构建关键点特征信息作为支持向量机（SVM）的输入数据。支持向量机算法是用 Python 程序语言设计并实时执行的。使用该算法，无人机采集的实时图像和滑坡预警信息将被发送到控制中心进行进一步分析。最后，通过现场实验验证了所提方法的有效性。通过深度信息和加速鲁棒特征（SURF）检测，构建关键点特征信息作为支持向量机（SVM）的输入数据。支持向量机算法是用 Python 程序语言设计并实时执行的。使用该算法，无人机采集的实时图像和滑坡预警信息将被发送到控制中心进行进一步分析。最后，通过现场实验验证了所提方法的有效性。无人机采集的实时图像和滑坡预警信息将发送到控制中心进行进一步分析。最后，通过现场实验验证了所提方法的有效性。无人机采集的实时图像和滑坡预警信息将发送到控制中心进行进一步分析。最后，通过现场实验验证了所提方法的有效性。